Responses of land evapotranspiration to Earth’s greening in CMIP5 Earth System Models

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Responses of land evapotranspiration to Earth’s greening in CMIP5 Earth System Models

Zhenzhong Zeng, Zaichun Zhu, Xu Li, Laurent Z X Li, Anping Chen, Xiaogang He, Shilong Piao

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Zhenzhong Zeng, Zaichun Zhu, Xu Li, Laurent Z X Li, Anping Chen, et al.. Responses of land

evapotranspiration to Earth’s greening in CMIP5 Earth System Models. Environmental Research

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Responses of land evapotranspiration to Earth’s greening in CMIP5 Earth System Models

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Environ. Res. Lett.11(2016)104006 doi:10.1088/1748-9326/11/10/104006

LETTER

Responses of land evapotranspiration to Earth ’ s greening in CMIP5 Earth System Models

Zhenzhong Zeng

¹

, Zaichun Zhu

¹

, Xu Lian

¹

, Laurent Z X Li

²

, Anping Chen

³

, Xiaogang He

⁴

and Shilong Piao

^1,5

1 Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing 100871, People’s Republic of China

2 Laboratoire de Météorologie Dynamique, Centre National de la Recherche Scientiﬁque, Université Pierre et Marie Curie-Paris 6, F-75252 Paris, France

3 The Woods Hole Research Center, Falmouth, MA 02540, USA

4 Department of Civil and Environmental Engineering, Princeton University, NJ 08544, USA

5 Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100085, People’s Republic of China E-mail:[email protected]

Keywords:evapotranspiration sensitivity to greening, Earth greening, leaf area index, land–climate interaction, Earth System Modelling Supplementary material for this article is availableonline

Abstract

Satellite-observed Earth’s greening has been reproduced by the latest generation of Earth System Models (ESMs) participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5). Land evapotranspiration (ET) is expected to rise with increasing leaf area index (LAI, Earth’s greening). The responses of ET play a key role in the land–climate interaction, but they have not been evaluated previously. Here, we assessed the responses of ET to Earth’s greening in these CMIP5 ESMs. We veriﬁed a signiﬁcant and positive response of ET to the modeled greening in each model. However, the responses were not comparable across the ESMs because of an inherent bias in the sensitivity of ET to LAI ( ¶ ET ¶ LAI ) in the models: ¶ ET ¶ LAI is precisely and inversely proportional to the trend of LAI

¶ ¶

( LAI t ) across the ESMs. Constrained by this inversely proportional relationship with the satellite- observed ¶ LAI ¶t , the Earth’s ¶ ET ¶ LAI is 0.26 (0.21–0.34) mm d

⁻¹

per m

²

m

⁻²

, equaling the independent estimates from satellite-derived reconstructions of ET and LAI. Thus, the Earth’s greening-induced acceleration of ET is about 11.4 mm yr

⁻¹

, accounting for more than 50% of the observed increase in land ET over the last 30 years. To better model the land–climate interaction,

¶ ET ¶ LAI in these ESMs should be calibrated. A feasible means is to improve the representation of the magnitude of LAI in these CMIP5 ESMs.

1. Introduction

The greening of the Earth over the last three decades has been documented by several studies based on the National Oceanic and Atmospheric Administration- Advanced Very High Resolution Radiometer (NOAA- AVHRR) satellite records (e.g., Nemani et al 2003, Zhu et al 2013, 2016, Dardel et al 2014, Piao et al 2015 ) , matching the long-term forest inventories ( McMahon et al 2010, Fang et al 2014) and enhanced seasonal exchange of CO

2

(Graven et al 2013, Forkel et al 2016).

The Earth ’ s greening, de ﬁ ned as an increase in leaf area index (LAI) over land, seems to have been reproduced by most state-of-the-art Earth System Models (ESMs) participating in the Coupled Model Intercomparison

Project Phase 5 (CMIP5) (Taylor et al 2012, Mahowald et al 2015 ) . Furthermore, these ESMs also unequivo- cally and consistently project continuation of Earth’s greening, at least until the end of the 21st century ( Mahowald et al 2015 ) . As the change in land surface properties has a profound impact on land–atmosphere exchanges of water and energy, and ultimately on the climate system, the modeled Earth’s greening should incorporate boundary forcing in these climate model simulations.

The key ﬂux determining the strength of the greening-induced boundary forcing is terrestrial eva- potranspiration ( ET ) . ET, as a central process in the climate system, represents the exchanges of energy and water between the land and the atmosphere. As for its

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driver, because transpiration through vegetation crown dominates terrestrial ET (Jasechko et al 2013), the greening of the Earth has been one of the most important drivers in the rise of global land ET over the past 30 years (Zhang et al 2015). As for its effect, terres- trial ET plays a fundamental role in shaping the cli- mate. It cools the land surface temperature by consuming more than half of the solar radiation absorbed by the land surface (Trenberth et al 2009), and drives atmospheric dynamics by the released latent heat during condensation ( Makarieva et al 2013). Therefore, the greening of the Earth would be expected to reshape the Earth’s climate (e.g., rain- fall, temperature, and circulation ) by producing eva- potranspiration (Shukla and Mintz 1982, Bounoua et al 2000, Sewall et al 2000, Buermann et al 2001).

In the ESMs, the response of ET to the modeled Earth’s greening determines the strength of vegetation feedback in the land–climate interaction. Assuming that all CMIP5 ESMs simulated the sensitivity of ET to LAI ( ¶ ET ¶ LAI ) following the laws of nature,

¶ ET ¶ LAI in these ESMs should be close to the Earth ’ s ¶ ET ¶ LAI, resulting in a tendency to be con- stant across the models. Terrestrial ET increased more in the ESMs with a higher greening rate ( ¶ LAI ¶ t ) ; thus a stronger evaporative cooling effect and faster moisture recycling were modeled. That is, greening- induced boundary forcing was underestimated (or overestimated ) by ESMs that modeled a weaker ( or stronger) greening rate than the satellite-observed greening rate. This implies that, to better model the land – climate interaction in the ESMs, the modeling community should focus on improving the simulation of vegetation dynamics (i.e., ¶ LAI ¶t ).

To our knowledge, however, no evaluation of the responses of ET to the Earth ’ s greening in these CMIP5 ESMs has been performed. If biases exist in the modeled ¶ ET ¶ LAI, the greening-induced boundary forcing would not be comparable across the ESMs. In this case, it is important to understand why the mod- eled ¶ ET ¶ LAI differs from the ESMs. In addition, the modeling community should pay more attention to calibration of the representation of the sensitivity of ET to greening (i.e., ¶ ET ¶ LAI).

In this study, we assessed the responses of ET to Earth’s greening in the CMIP5 ESMs by integrating the historical simulations from CMIP5 ESMs with satellite-derived reconstructions of ET and LAI over the last 30 years. The objective was to address the fol- lowing questions (1) is there a signiﬁcant and positive response of land ET to Earth ’ s greening in each ESM?

A positive answer to this question would verify that land LAI is an important driver of the interannual var- iation of land ET in the ESMs. ( 2 ) Is ¶ ET ¶ LAI con- stant across the ESMs? If the answer is positive, we would expect a greater increase in land ET in the ESMs with a higher greening rate. A negative answer would prompt us to ask, (3) why does the modeled

¶ ET ¶ LAI differ among the CMIP5 ESMs, how great

is the Earth ’ s ¶ ET ¶ LAI, and how can we calibrate the modeled ¶ ET ¶ LAI to better model the land – climate interaction?

2. Methods and datasets

2.1. Model datasets

We made use of the historical simulations by the latest generation of ESMs that participated in CMIP5 ( Taylor et al 2012, Stocker et al 2013 ) . A summary of the CMIP5 ESMs used in this study is provided in table 1. A total of 27 ESMs from 14 modeling centers were chosen based on their data availability. The outputs of both LAI (in m

²

m

⁻²

) and ET (in mm d

⁻¹

) in the historical simulations of these ESMs were downloaded from the CMIP5 archive (http://pcmdi9.

llnl.gov/). The annual area-weighted global land- average LAI and ET during 1982–2005 were calculated using the average of all initial condition ensemble members available in the archive.

2.2. Observational datasets

The long-term NOAA-AVHRR satellite measure- ments were used to generate an 8 km global LAI product from 1982 to 2011 (AVHRR GIMMS LAI3g) (Zhu et al 2013). The quality of the satellite LAI product used in this study has been extensively evaluated through the comparisons with ﬁeld mea- surements, with an accuracy of about 0.68 m

²

m

⁻²

(root mean square error) relative to ﬁeld measured LAI (Zhu et al 2013). This product has been validated as a research-quality dataset (e.g., Zhu et al 2013, Pfeifer et al 2014) and has been extensively used in the research of long-term vegetation dynamics (e.g., Zhu et al 2013, 2016, Anav et al 2013a, Dardel et al 2014, Piao et al 2015, Sitch et al 2015).The annual area- weighted global land-average LAI during 1982–2011 was calculated from this dataset.

No direct observations of global land ET over the last 30 years have been reported. In this study, we included five satellite-derived reconstructions of long- term evapotranspiration over land: FLUXNET-MTE ET (1982–2008, based on eddy-covariance measure- ments Jung et al 2010), GRACE-MTE ET (1982–2011, based on water mass balance Zeng et al 2014), and three offline evapotranspiration algorithm-based pro- ducts, MPM ET (Zhang et al 2010), P-LSH ET (Zhang et al 2015), and PML ET (Zhang et al 2016) (details see table 2). All these products have been proven of quality for scientific researches. Yet, because of the lack of direct global observations, it is hard to estimate the biases in these products and to tell which product is superior to the others. Thus, all these products were applied to calculate the annual area-weighted global land-average ET of the last three decades, with the dif- ference among them representing an uncertainty range in the observed land ET. The Earth’s sensitivity 2

Environ. Res. Lett.11(2016)104006

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of ET to LAI ( ¶ ET ¶ LAI ) was thus estimated with these observed land ET and LAI over the last 30 years.

3. Results and discussion

3.1. Response of land ET to Earth’s greening in each CMIP5 ESM

The magnitude of land LAI found among the CMIP5 ESMs covered a large spectrum, and most models overestimated the magnitude of land LAI compared to observations from the AVHRR satellites (figure 1(a)), probably due to the systematic underestimate of NPP in these models ( Shao et al 2013, Anav et al 2013b ) . Despite this, the greening of the Earth, defined as a significant and positive trend of land LAI observed from the AVHRR satellites for the last 30 years (e.g., Zhu et al 2013, 2016, Piao et al 2015 ) , was reproduced by 16 / 27 CMIP5 ESMs (fi gure 1 ( b )) . For the other 11 / 27 ESMs, land LAI either did not vary annually (ACCESS1-0, ACCESS1-3, FIO-ESM), decreased

unreasonably by a constant rate each year ( MIROC5, fi gure S1 ) , or did not change signi fi cantly over the last 30 years ( CESM1-WACCM, CanESM2, GFDL- ESM2M, MIROC-ESM, MPI-ESM-LR, NorESM1- ME, inmcm4 ) . Land LAI, if not fi xed in models, is a key driving factor of interannual variation of land ET in all the models (figures 1(c) and S2). As the goal of this study is to investigate the responses of land ET to Earth’s greening in CMIP5 ESMs, we analyzed the responses of land ET to increasing LAI in the 16 CMIP5 ESMs that successfully reproduced the Earth’s greening of the last 30 years (figure 1(c)).

As shown in fi gure 1 ( c ) , there was a signi fi cant interannual correlation between the modeled land LAI and ET for all 16 ESMs that reproduced the Earth ’ s greening. The strongest correlation between the mod- eled land LAI and ET was found in GFDL-CM3 (figure 1(c5), R=0.81, P<0.01), and the weakest correlation was found in CESM1-CAM5 (figure 1(c4), R=0.35, P<0.1). Consistent with the significant correlation, land ET increased significantly with

Table 1.Summary of the CMIP5 Earth System Models analyzed in this study.

Model name Modeling center Institute

ACCESS1-0 CSIRO-BOM CSIRO(Commonwealth Scientiﬁc and Industrial Research Organisation, Australia), and BOM(Bureau of Meteorology, Australia)

ACCESS1-3

BNU-ESM GCESS College of Global Change and Earth System Science, Beijing Normal University

CCSM4 NCAR National Center for Atmospheric Research

CESM1-BGC NSF-DOE-NCAR National Science Foundation, Department of Energy, National Center for Atmospheric Research

CESM1-CAM5 CESM1-WACCM

CanESM2 CCCma Canadian Centre for Climate Modelling and Analysis

FIO-ESM FIO The First Institute of Oceanography, SOA, China

GFDL-CM3 NOAA GFDL Geophysical Fluid Dynamics Laboratory GFDL-ESM2G

GFDL-ESM2M

HadGEM2-CC MOHC Met Ofﬁce Hadley Centre(additional HadGEM2-ES realizations contributed by Instituto Nacional de Pesquisas Espaciais)

HadGEM2-ES

IPSL-CM5A-LR IPSL Institut Pierre-Simon Laplace

IPSL-CM5A-MR IPSL-CM5B-LR

MIROC-ESM MIROC Atmosphere and Ocean Research Institute(The University of Tokyo), National Institute for Environmental Studies, and Japan Agency for Marine-Earth Science and Technology MIROC-ESM-CHEM

MIROC5

MPI-ESM-LR MPI-M Max Planck Institute for Meteorology(MPI-M) MPI-ESM-MR

NorESM1-M NCC Norwegian Climate Centre

NorESM1-ME

bcc-csm1-1 BCC Beijing Climate Center, China Meteorological Administration bcc-csm1-1-m

inmcm4 INM Institute for Numerical Mathematics

3

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Table 2.Descriptions of theﬁve long-term global land ET products used in this study.

product reference period algorithm drivers

FLUXNET-MTE Junget al(2010) 1982–2008 Combined with FLUXNET and the model tree ensemble Climate: precipitation, temperature Vegetation: fAPAR

GRACE-MTE Zenget al(2014) 1982–2011 Combined with water balance and the model tree ensemble Climate: precipitation, temperature, radiation, pressure, vapor pressure, wind speed, wet day frequency, frost day frequency

Vegetation: NDVI

MPM Zhanget al(2010) 1983–2006 Modiﬁed Penman–Monteith approach Climate: radiation, temperature, air water vapor pressure Vegetation: NDVI

P-LSH Zhanget al(2015) 1982–2013 Process-based Land Surface Evapotranspiration Heat Fluxes algorithm

Climate: radiation, temperature, air water vapor pressure, wind speed, vegetation, CO2

Vegetation: NDVI

PML Zhanget al(2016) 1981–2012 Penman–Monteith–Leuning model Climate: precipitation, air temperature, vapor pressure, shortwave downward radiation, longwave downward radiation and wind speed

Vegetation: LAI, emissivity and albedo

4

Environ.Res.Lett.11(2016)104006

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increasing LAI in all 16 ESMs (ﬁ gure 1 ( c )) . That is, land ET responded positively to the modeled Earth ’ s greening in all of the models. Land LAI indeed is a key driver of the interannual variability in land ET in each ESM ( e.g., Zhang et al 2015 ) .

In theory, LAI is one of the key parameters of land ET which is also co-determined by factors like soil moisture supply, solar radiation, and wind speed. LAI could change land ET by its role in the regulations of the surface area of vegetation in direct contact with the atmosphere and thus the efﬁciency by which water can be transferred from within the vegetation to the atmos- phere (e.g., canopy conductance g

_c

) , surface radiation budget ( e.g., albedo, and radiation partitioning between canopy and soil ) , boundary layer aerodynamic char- acteristics (e.g., aerodynamic conductance), and redis- tribution of rainfall ( e.g., interception and through fall ) . Among them, regulating the canopy conductance ( ) g

_c

has been suggested as a dominant one by dozens of stu- dies ( e.g., Sellers et al 1997, Zeng et al 1999, Bonan 2002, Krinner et al 2005, Kala et al 2013, Zhang et al 2015 ) . The regulation of LAI on the canopy conductance ( ) g

_c

is often formatted as g

_c

= g

_{s max}

b ( ) ( w f LAI , ) where g

_{s max}

is the leaf-level maximum conductance, and b ( ) w is a soil moisture stress scalar. Based on the results

of ﬁ eld experiments, the theoretical function f ( LAI ) is widely described in land surface models (e.g., Zeng et al1999, Krinner et al 2005, Oleson et al2010 ) as

=

+

( ) ( )

f

i i

LAI 1

1 LAI

1

1 2

or

= -

^{- ⋅}

( ) ( ) ( )

f LAI 1 e

^{j LAI}

j , 2

where i

₁

, i

₂

, and j are parameters that are dependent on aerodynamic roughness and vegetation type.

Figure 2 shows the curves of equations (1) and (2) with i

1

= 1, i

2

= 1, and j=1, which clearly demonstrates that, in each ESM, land ET should increase with LAI.

3.2. Responses of land ET to Earth’s greening across CMIP5 ESMs

As 1) land ET responds to the modeled greening in each ESM (ﬁgures 1(c)) and 2) the greening rate (i.e.,

¶ LAI ¶ t ) differs among the models (ﬁ gure 1 ( b )) , the increase in land ET ( ¶ ET ¶ t ) is expected to be greater in the ESMs with a higher greening rate, i.e.,

¶

¶ µ ¶

¶

t t

ET LAI

. However, as shown in ﬁgure 3, there is no correlation between the modeled ¶ ET ¶t and

Figure 1.Trend of land LAI and its interannual correlation with land ET in CMIP5 ESMs.(a)Time series of global land LAI during 1982–2011 from observations(AVHRR GIMMS LAI3g)and CMIP5 historical simulations.(b)Trend of global land LAI for the last 30 years.^***,P<0.01;^**,P<0.05.(c0)Interannual variation of land LAI and land ET for the last 30 years reconstructed from satellite observations.(c1)–(c16)Interannual correlation between land LAI and land ET in each ESM that reproduces a signiﬁcant and positive trend in land LAI for the last 30 years. Inset number is the interannual correlation coefﬁcient(R)between land LAI and land ET.

5

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¶ LAI ¶t across the ESMs (R=0.03, P=0.90).

Although ¶ LAI ¶t differs among the models,

¶ ET ¶t remains constant (∼ 0.01 mm d

⁻¹

per decade ) across models (ﬁgure 3), indicating that the sensitivity of land ET to land LAI ( ¶ ET ¶ LAI ) varies across the ESMs.

Indeed, we found an inherent bias in the modeled

¶ ET ¶ LAI that causes the lack of correlation between

¶ LAI ¶t and ¶ ET ¶t across the ESMs (ﬁgure 4).

¶ ET ¶ LAI is signi ﬁ cantly and inversely proportional to ¶ LAI ¶t across the CMIP5 ESMs. That is,

¶

¶ = ¶

¶t ET

LAI 0.01 LAI

( R = 0.91, P < 0.01; ﬁ gure 4 ) . Given that ¶

¶ = ¶

¶ ⋅ ¶

¶

t t

ET ET

LAI

LAI = ¶

¶ ⋅

⎜ ⎟

⎛

⎝

⎞

⎠ 0.01 LAI t

¶

¶ = t

LAI 0.01 mm d

⁻¹

per decade, ¶ ET ¶t across the ESMs tends to be constant, as shown in ﬁ gure 3. This explains the lack of correlation between ¶ ET ¶ LAI and ¶ LAI ¶t across the ESMs (ﬁgure 3).

We then investigated why ¶ ET ¶ LAI is inversely proportional to ¶ LAI ¶t across the ESMs. In theory, as shown in ﬁgure 2, while ET increases with LAI in each ESM, ¶ ET ¶ LAI decreases with the magnitude of LAI across the ESMs, which is also shown in the par- tial derivatives to LAI for equations (1) and (2):

¶

¶ µ ¶

¶ =

+

( )

( ) ( )

f i

i i

ET LAI

LAI

¹

LAI 3

1 2 2

and

¶

¶ µ ¶

¶ ( ) =

^{- ⋅}

f ( )

ET LAI

LAI

LAI e

^{j LAI}

. 4

Thus, the inherent bias of ¶ ET ¶ LAI is primarily due to the bias in the magnitude of LAI across the CIMP5 ESMs (figure 1(a)). In addition, the bias in the magnitude of LAI is also responsible for the difference in ¶ LAI ¶t across the models: ¶ LAI ¶t is signifi- cantly proportional to the magnitude of land LAI across the models (P<0.01; figure 5). As a result,

¶ ET ¶ LAI is inversely proportional to ¶ LAI ¶t across the CMIP5 ESMs (ﬁgure 4).

3.3. The Earth’s sensitivity of land ET to land LAI If all the ESMs modeled the land – climate interaction well, the modeled sensitivity of land ET to land LAI should be almost constant across the models, with the constant being the Earth ’ s ¶ ET ¶ LAI. However, due to the bias in the magnitude of modeled LAI (ﬁgure 1(a)), there are biases in the modeled

¶ ET ¶ LAI in the CMIP5 ESMs. To better model the land – climate interaction, ¶ ET ¶ LAI in these ESMs should be calibrated to equal the Earth’s ¶ ET ¶ LAI.

Figure 2.Examples of the negative exponential curve used to represent canopy conductance,g_c,as a function of LAI for parameters

=

i1 1,i2=1,j=1 in equations(1)and(2).

Figure 3.Trend of global land ET versus trend of global land LAI across the CMIP5 ESMs.

6

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Here, we further applied two approaches to provide a reference for the Earth ’ s ¶ ET ¶ LAI making use of model simulations from CMIP5 and satellite observa- tions of ET and LAI over the last 30 years.

First, the Earth’s ¶ ET ¶ LAI can be estimated by the observational constraints on the precise inversely proportional relationship between the modeled

¶ ET ¶ LAI and ¶ LAI ¶t across the CMIP5 ESMs. As

Figure 4.Sensitivity of land ET to land LAI versus(a)trend in global land LAI, and versus(b)inverse of trend in global land LAI across the CMIP5 ESMs.

Figure 5.Trend of global land LAI versus magnitude of global land LAI across the CMIP5 ESMs.(a)Absolute trend of land LAI, and (b)relative trend of land LAI.

7

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shown in ﬁgure 4, the modeled ¶ ET ¶ LAI ranges from 0.11 to 1.37 mm d

⁻¹

per m

²

m

⁻²

in the ESMs, and the inversely proportional relationship across the ESMs is ¶

¶ = ¶

¶t ET

LAI 0.01 LAI

( R = 0.91, P < 0.01;

ﬁ gure 4 ) . The satellite-observed trend of land LAI over the last 30 years, i.e., ¶ LAI ¶t , is 0.04 ± 0.01 m

²

m

⁻²

per decade (ﬁ gure 1 ( b )) ( Zhu et al 2013 ) . We con- strained the modeled inversely proportional relation- ship with the observed ¶ LAI ¶t , and estimated the Earth’s ¶ ET ¶ LAI to be 0.26 (0.21–0.34) mm d

⁻¹

per m

²

m

⁻²

(red bar in ﬁgure 6).

Second, we applied the satellite-derived reconstruc- tions of ET and LAI during the last 30 years to provide an independent estimate of the Earth ’ s ¶ ET ¶ LAI. The annual area-weighted global land-average ET and LAI for the last 30 years were substituted into the linear regression equation, ET = k

₁

LAI + c

₁

, where k

₁

is the sensitivity. The observed ¶ ET ¶ LAI is 0.18±0.05 mm d

⁻¹

per m

²

m

⁻²

using FLUXNET-MTE ET ( Jung et al 2010 ) , 0.72 ± 0.10 mm d

⁻¹

per m

²

m

⁻²

using GRACE-MTE ET ( Zeng et al 2014 ) , 0.38 ±

0.05 mm d

⁻¹

per m

²

m

⁻²

using MPM ET (Zhang et al 2010), 0.42±0.06 mm d

⁻¹

per m

²

m

⁻²

using P-LSH ET (Zhang et al 2015), and 0.43±

0.04 mm d

⁻¹

per m

²

m

⁻²

using PML ET (Zhang et al 2016) (light blue bars in ﬁgure 6). As the observed LAI and ET could vary with other factors simulta- neously, we further calculated the observed ¶ ET ¶ LAI with the linear regression equation controlling pre- cipitation and temperature, i.e., ET = k

₂

LAI + c P

₂

+ c

₃

T + c

₄

, where P and T are the observed annual precipitation and annual average temperature from the CRU dataset, respectively (Harris et al 2014), and k

2

is the sensitivity controlling precipitation and temp- erature. In this approach, the observed ¶ ET ¶ LAI ranges from 0.08 to 0.39 mm d

⁻¹

per m

²

m

⁻²

depend- ing on ET products ( green bars in ﬁ gure 6 ) . The ensemble of the observed ¶ ET ¶ LAI controlling pre- cipitation and temperature provides the optimal esti- mate of the Earth’s ¶ ET ¶ LAI, namely 0.29

(0.25–0.33) mm d

⁻¹

per m

²

m

⁻²

(dark green bar in ﬁ gure 6 ) .

Thus, the two independent estimates of the Earth’s ¶ LAI ¶t match very well with each other.

Using the sensitivity estimated by the observational constraints, the satellite-observed Earth ’ s greening (i.e., the increasing LAI at a rate of 0.04 ± 0.01 m

²

m

⁻²

per decade ) has accelerated land ET by 11.4 mm yr

⁻¹

in the past 30 years. The total increase in land ET dur- ing the last 30 years is 16.3±6.9 mm yr

⁻¹

according to the CMIP5 ESMs, and ranges from 13.1 to 51.2 mm yr

⁻¹

according to the satellite-derived recon- structions of ET (average: 26.6 mm yr

⁻¹

), depending on the model and/or ET dataset used as a reference.

Therefore, the Earth ’ s greening-induced acceleration of ET has contributed more than 50% of the observed increase in land ET over the last 30 years. The greater capacity of water loss associated with increasing LAI has become a dominant driver of the increasing land ET in the past 30 years.

3.4. Implications for model improvement

Considering the key role of ET in the water cycle and energy ﬂ uxes, the biophysical feedback of vegetation growth activity should play an important role in shaping the climate. However, in the CMIP5 ESMs, the biophy- sical feedback of vegetation growth activity has not been represented well due to the biases in the greening rate

¶ ¶

( LAI t ) and the sensitivity of ET to greening

¶ ¶

( ET LAI . ) The modeling community should cali- brate the modeled ¶ ET ¶ LAI to make it equivalent to the Earth’s ¶ ET ¶ LAI (∼0.26 mm d

⁻¹

per m

²

m

⁻²

).

As the biases of both ¶ LAI ¶t and ¶ ET ¶ LAI are primarily caused by the modeled bias of the magnitude of land LAI, feasible and effective methods for improv- ing the representation of vegetation – climate feedback are therefore required to improve the representation of the magnitude of LAI in these state-of-the-art ESMs.

In addition, we found that magnitude of global land LAI of four ESMs was similar to the value observed by satellite (MPI-ESM-MR, IPSL-CM5B-LR,

Figure 6.The Earth’s sensitivity of land ET to land LAI estimated from the observational constraint on the CMIP5 ESMs(red bar), and the linear regressions of satellite-derived reconstructions of ET and LAI over the last 30 years(blue and green bars). For the sensitivity from the observational constraint, the error bar shows the range constrained by the mean±standard error(SE)of the satellite- observed trend in global land LAI. For the sensitivities of the regressions,^***,P<0.01; n.s.,P>0.05 for sensitivity, and error bars show the standard errors of the sensitivities.

8

(11)

IPSL-CM5A-LR, IPSL-CM5A-MR, see ﬁ gure 5; the observed magnitude is 1.5 m

²

m

⁻²

) . However, all of these models still underestimated the trend in global land LAI compared to the satellite-observed trend ( i.e., 0.04±0.01 m

²

m

⁻²

per decade). Among these four models, the closer the modeled ¶ LAI ¶t is to the satellite-observed trend, the closer the modeled

¶ ET ¶ LAI is to the Earth’s ¶ ET ¶ LAI (ﬁgure 4(a)).

Therefore, another work at a next stage for the model- ing community is to investigate the mechanisms driv- ing the temporal changes in land LAI ( e.g., Piao et al 2015, Zhu et al 2016 ) and then improve the repre- sentation of temporal variation of LAI in the ESMs.

4. Conclusions

Our results demonstrated a signiﬁcant and positive response of land ET to increasing LAI in all of the 16 CMIP5 ESMs that reproduced the Earth’s greening for the last three decades. However, the responses of land ET to the modeled greening are not comparable across the ESMs due to an inherent bias in the modeled

¶ ET ¶ LAI: ¶ ET ¶ LAI is precisely and inversely proportional to ¶ LAI ¶t across the ESMs. Further- more, this inherent bias in the modeled sensitivity was found to be primarily due to the bias in the magnitude of LAI. The bias in the modeled ¶ ET ¶ LAI indicates that greening-induced biophysical feedback has not been represented well in these ESMs. Thus, it is necessary to improve the representation of the magni- tude of LAI, which is an easy, feasible, and effective means for an ESM to calibrate the response of land ET to greening and thus to better represent the climate effect of Earth ’ s greening in the model.

We estimated the Earth’s ¶ ET ¶ LAI with two independent approaches, including the observational constraints on the precise inversely proportional rela- tionship between the modeled ¶ ET ¶ LAI and

¶ LAI ¶t across the CMIP5 ESMs, and linear regres- sion of the satellite-derived reconstructions of ET and LAI during the last 30 years. The suggested sensitivity of land ET to land LAI in the Earth ’ s climate is

~ 0.26 mm d

⁻¹

per m

²

m

⁻²

. With this sensitivity, the satellite-observed Earth’s greening can be translated into acceleration of land ET by a rate of 3.8 mm yr

⁻¹

per decade, accounting for more than 50% of the observed acceleration rate of land ET over the last 30 years. As the response of land ET deeply affects the climate (water cycle and energy ﬂuxes), it will be important to investi- gate the climate effect of Earth’s greening by combining satellite-derived observations and the state-of-the-art ESMs. Furthermore, because the biophysical feedback induced by the response of land ET is dominated over the regions where vegetation has changed, the under- standing of the climate effect of Earth ’ s greening would improve future projections of regional climate change and beneﬁt local policy decisions.

Acknowledgments

We are grateful to the World Climate Research Programme Working Group on Coupled Modelling and the climate modeling groups listed in table 1 for the model outputs of CMIP5. This study was sup- ported by National Natural Science Foundation of China (41530528), and the 111 Project (B14001).

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